Introduction¶

This dataset was scraped from nextspaceflight.com and includes all the space missions since the beginning of Space Race between the USA and the Soviet Union in 1957!

Install Package with Country Codes¶

In [1]:
%pip install iso3166

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: iso3166 in /home/mitresh/.local/lib/python3.10/site-packages (2.1.1)
Note: you may need to restart the kernel to use updated packages.

Upgrade Plotly¶

Run the cell below if you are working with Google Colab.

In [2]:
%pip install --upgrade plotly

Defaulting to user installation because normal site-packages is not writeable
Requirement already satisfied: plotly in /home/mitresh/.local/lib/python3.10/site-packages (5.15.0)
Requirement already satisfied: tenacity>=6.2.0 in /home/mitresh/.local/lib/python3.10/site-packages (from plotly) (8.2.2)
Requirement already satisfied: packaging in /home/mitresh/.local/lib/python3.10/site-packages (from plotly) (23.1)
Note: you may need to restart the kernel to use updated packages.

Import Statements¶

In [3]:
import numpy as np
import pandas as pd
import plotly.express as px
import matplotlib.pyplot as plt
import seaborn as sns

# These might be helpful:
from iso3166 import countries
from datetime import datetime, timedelta

Notebook Presentation¶

In [4]:
pd.options.display.float_format = '{:,.2f}'.format

Load the Data¶

In [5]:
df_data = pd.read_csv('mission_launches.csv')

Preliminary Data Exploration¶

  • What is the shape of df_data?
  • How many rows and columns does it have?
  • What are the column names?
  • Are there any NaN values or duplicates?
In [6]:
df_data.head()
Out[6]:
Unnamed: 0.1 Unnamed: 0 Organisation Location Date Detail Rocket_Status Price Mission_Status
0 0 0 SpaceX LC-39A, Kennedy Space Center, Florida, USA Fri Aug 07, 2020 05:12 UTC Falcon 9 Block 5 | Starlink V1 L9 & BlackSky StatusActive 50.0 Success
1 1 1 CASC Site 9401 (SLS-2), Jiuquan Satellite Launch Ce... Thu Aug 06, 2020 04:01 UTC Long March 2D | Gaofen-9 04 & Q-SAT StatusActive 29.75 Success
2 2 2 SpaceX Pad A, Boca Chica, Texas, USA Tue Aug 04, 2020 23:57 UTC Starship Prototype | 150 Meter Hop StatusActive NaN Success
3 3 3 Roscosmos Site 200/39, Baikonur Cosmodrome, Kazakhstan Thu Jul 30, 2020 21:25 UTC Proton-M/Briz-M | Ekspress-80 & Ekspress-103 StatusActive 65.0 Success
4 4 4 ULA SLC-41, Cape Canaveral AFS, Florida, USA Thu Jul 30, 2020 11:50 UTC Atlas V 541 | Perseverance StatusActive 145.0 Success
In [7]:
df_data.shape
Out[7]:
(4324, 9)
In [8]:
df_data.columns
Out[8]:
Index(['Unnamed: 0.1', 'Unnamed: 0', 'Organisation', 'Location', 'Date',
       'Detail', 'Rocket_Status', 'Price', 'Mission_Status'],
      dtype='object')
In [9]:
df_data.isna().values.any()
Out[9]:
True

Data Cleaning - Check for Missing Values and Duplicates¶

Consider removing columns containing junk data.

In [10]:
columns_to_drop = ['Unnamed: 0.1','Unnamed: 0']
df = df_data.drop(columns_to_drop, axis=1)
In [11]:
df.head()
Out[11]:
Organisation Location Date Detail Rocket_Status Price Mission_Status
0 SpaceX LC-39A, Kennedy Space Center, Florida, USA Fri Aug 07, 2020 05:12 UTC Falcon 9 Block 5 | Starlink V1 L9 & BlackSky StatusActive 50.0 Success
1 CASC Site 9401 (SLS-2), Jiuquan Satellite Launch Ce... Thu Aug 06, 2020 04:01 UTC Long March 2D | Gaofen-9 04 & Q-SAT StatusActive 29.75 Success
2 SpaceX Pad A, Boca Chica, Texas, USA Tue Aug 04, 2020 23:57 UTC Starship Prototype | 150 Meter Hop StatusActive NaN Success
3 Roscosmos Site 200/39, Baikonur Cosmodrome, Kazakhstan Thu Jul 30, 2020 21:25 UTC Proton-M/Briz-M | Ekspress-80 & Ekspress-103 StatusActive 65.0 Success
4 ULA SLC-41, Cape Canaveral AFS, Florida, USA Thu Jul 30, 2020 11:50 UTC Atlas V 541 | Perseverance StatusActive 145.0 Success

Descriptive Statistics¶

In [12]:
df.describe()
Out[12]:
Organisation Location Date Detail Rocket_Status Price Mission_Status
count 4324 4324 4324 4324 4324 964 4324
unique 56 137 4319 4278 2 56 4
top RVSN USSR Site 31/6, Baikonur Cosmodrome, Kazakhstan Wed Nov 05, 2008 00:15 UTC Cosmos-3MRB (65MRB) | BOR-5 Shuttle StatusRetired 450.0 Success
freq 1777 235 2 6 3534 136 3879

Number of Launches per Company¶

Create a chart that shows the number of space mission launches by organisation.

In [13]:
launch_count = df.Organisation.value_counts()
launch_count = launch_count.sort_values(ascending=False)
In [14]:
plt.figure(figsize=(20, 12)) 
plt.barh(launch_count.index, launch_count.values)
plt.ylabel('Organisation', fontsize=5)
plt.xlabel('Number of Space Mission Launches')
plt.title('Number of Space Mission Launches by Organisation')
plt.show()

Number of Active versus Retired Rockets¶

How many rockets are active compared to those that are decomissioned?

In [15]:
active_rockets = df[df['Rocket_Status'] == 'StatusActive'].shape[0]
active_rockets = df['Rocket_Status'].value_counts()
In [16]:
plt.barh(active_rockets.index, active_rockets.values)
plt.ylabel('Rocket Status', fontsize=12)
plt.xlabel('Number of Rockets')
plt.title('Number of Active versus Retired Rockets')
plt.show()

Distribution of Mission Status¶

How many missions were successful? How many missions failed?

In [17]:
mission_status = df[df['Mission_Status'] == 'Success'].shape[0]
mission_status = df['Mission_Status'].value_counts()
In [18]:
plt.barh(mission_status.index, mission_status.values)
plt.ylabel('Mission Status', fontsize=12)
plt.xlabel('Number of Missions')
plt.title('Distribution of Mission Status')
plt.show()

How Expensive are the Launches?¶

Create a histogram and visualise the distribution. The price column is given in USD millions (careful of missing values).

In [19]:
df_clean = df.dropna(subset=['Price'])
df_clean.Price.isna().values.any()
Out[19]:
False
In [20]:
plt.figure(figsize=(8, 6))
plt.hist(df_clean["Price"], bins=10, edgecolor='black')

plt.xlabel('Price (USD millions)')
plt.xticks(rotation=90)
plt.ylabel('Frequency')
plt.title('Distribution of Prices')
plt.show()

Use a Choropleth Map to Show the Number of Launches by Country¶

  • Create a choropleth map using the plotly documentation
  • Experiment with plotly's available colours. I quite like the sequential colour matter on this map.
  • You'll need to extract a country feature as well as change the country names that no longer exist.

Wrangle the Country Names

You'll need to use a 3 letter country code for each country. You might have to change some country names.

  • Russia is the Russian Federation
  • New Mexico should be USA
  • Yellow Sea refers to China
  • Shahrud Missile Test Site should be Iran
  • Pacific Missile Range Facility should be USA
  • Barents Sea should be Russian Federation
  • Gran Canaria should be USA

You can use the iso3166 package to convert the country names to Alpha3 format.

Use a Choropleth Map to Show the Number of Failures by Country¶

In [21]:
df['Country'] = df['Location'].str.split(',').str[-1].str.strip()
df_sorted = df.sort_values('Country')
In [22]:
fig = px.choropleth(df_sorted, locations='Country', locationmode='country names', color='Mission_Status',
                    projection='natural earth')
fig.update_layout(title_text='Choropleth Map', title_x=0.5)

Create a Plotly Sunburst Chart of the countries, organisations, and mission status.¶

In [23]:
fig = px.sunburst(df, path=['Location', 'Organisation', 'Mission_Status'])
fig.update_layout(title_text='Sunburst Chart - Countries, Organisations, and Mission Status')
fig.show()

Analyse the Total Amount of Money Spent by Organisation on Space Missions¶

In [24]:
total_spending = df.groupby('Organisation')['Price'].sum()
print(total_spending)

Organisation
AEB                                                                 0
AMBA                                                                0
ASI                                                                 0
Arianespace         48.5200.048.5200.0200.0200.037.0200.037.0200.0...
Arm??e de l'Air                                                     0
Blue Origin                                                         0
Boeing                            133.0164.0164.0350.0133.0133.0164.0
CASC                29.7564.6829.1529.7564.6829.1529.7530.829.755....
CASIC                                                               0
CECLES                                                              0
CNES                                                                0
Douglas                                                             0
EER                                                              20.0
ESA                                                              37.0
Eurockot            41.841.841.841.841.841.841.841.841.841.841.841...
ExPace                                                           28.3
Exos                                                                0
General Dynamics                                                    0
IAI                                                                 0
ILS                 65.065.065.065.065.0115.0153.0109.0130.0135.01...
IRGC                                                                0
ISA                                                                 0
ISAS                                                                0
ISRO                21.031.062.021.021.047.021.062.021.031.047.031...
JAXA                                                     90.039.039.0
KARI                                                                0
KCST                                                                0
Khrunichev                                                          0
Kosmotras           29.029.029.029.029.029.029.029.029.029.029.029...
Land Launch                                                         0
Landspace                                                           0
Lockheed                             35.035.035.035.035.035.035.035.0
MHI                 112.590.0112.590.0112.590.090.090.090.090.090....
MITT                                                                0
Martin Marietta              35.035.0136.635.0136.6136.6136.635.035.0
NASA                450.0450.0450.0450.0450.0450.0450.0450.0450.04...
Northrop            46.085.085.040.085.085.085.085.045.046.040.085...
OKB-586                                                             0
OneSpace                                                            0
RAE                                                                 0
RVSN USSR                                              5,000.05,000.0
Rocket Lab                    7.57.57.57.57.57.57.57.57.57.57.57.57.5
Roscosmos           65.048.548.548.565.048.548.565.048.565.065.048...
SRC                                                                 0
Sandia                                                           15.0
Sea Launch                                                          0
SpaceX              50.050.050.050.050.050.050.050.050.050.050.050...
Starsem                                                             0
ULA                 145.0120.0153.0115.0164.0153.0350.0153.0350.01...
US Air Force        59.059.059.059.059.059.059.059.059.059.059.059...
US Navy                                                             0
UT                                                                  0
VKS RF              48.541.848.548.541.848.565.041.848.565.035.065...
Virgin Orbit                                                     12.0
Yuzhmash                                                            0
i-Space                                                             0
Name: Price, dtype: object

Analyse the Amount of Money Spent by Organisation per Launch¶

In [25]:
money_spent = df_data[df_data["Price"].notna()].copy()
money_spent["Price"] = money_spent["Price"].str.replace(',', '').astype(float)
In [26]:
organisation_expense = money_spent.groupby("Organisation")["Price"].mean().reset_index()
organisation_expense.sort_values("Price", ascending=False)
organisation_expense.head()
Out[26]:
Organisation Price
0 Arianespace 170.26
1 Boeing 177.29
2 CASC 40.13
3 EER 20.00
4 ESA 37.00

Chart the Number of Launches per Year¶

In [95]:
import datetime

def extract_year(date_str):
    try:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y %H:%M %Z")
    except ValueError:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y")
    return dt.year

df['Year'] = df['Date'].apply(extract_year)
In [96]:
ds = df['Year'].value_counts().reset_index()
ds.columns = [
    'Year', 
    'Count'
]
fig = px.bar(
    ds, 
    x='Year', 
    y="Count", 
    orientation='v', 
    title='Number Of launches Per Year' 
)
fig.show()

Chart the Number of Launches Month-on-Month until the Present¶

Which month has seen the highest number of launches in all time? Superimpose a rolling average on the month on month time series chart.

In [128]:
def extract_year(date_str):
    try:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y %H:%M %Z")
    except ValueError:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y")
    return dt.month

df['Month'] = df['Date'].apply(extract_year)
In [98]:
month_on_month = df['Month'].value_counts().reset_index()
month_on_month.columns = [
    'Month', 
    'Count'
]
fig = px.bar(
    month_on_month, 
    x='Month', 
    y="Count", 
    orientation='v', 
    title='Sum of total missions in each Month',
    color='Count'
)
fig.show()

Launches per Month: Which months are most popular and least popular for launches?¶

Some months have better weather than others. Which time of year seems to be best for space missions?

In [99]:
month_on_month.max()
Out[99]:
Month     12
Count    450
dtype: int64
In [100]:
month_on_month.min()
Out[100]:
Month      1
Count    268
dtype: int64

How has the Launch Price varied Over Time?¶

Create a line chart that shows the average price of rocket launches over time.

In [101]:
df_price = df.sort_values(by='Price', ascending=False)
In [102]:
fig = px.line(df_price, x='Price', y='Year', title='Sales Trend')
fig.show()
In [106]:
avg_price = df[df["Price"].notna()]
pd.options.mode.chained_assignment = None
avg_price["Price"] = avg_price["Price"].str.replace(',', '').astype(float)

avg_price.drop(columns=['Detail','year', 'Location', 'Organisation', 'Country','Date','Rocket_Status','Mission_Status'], inplace=True)
avg_price.head()
Out[106]:
Price Year Month
0 50.00 2020 8
1 29.75 2020 8
3 65.00 2020 7
4 145.00 2020 7
5 64.68 2020 7
In [108]:
avg_price.groupby("Year").mean().plot(figsize=(12, 8))
Out[108]:
<Axes: xlabel='Year'>
In [73]:
groups = avg_price.groupby("Year")
fig, ax = plt.subplots(figsize=(12, 8))
for year, group in groups:
    group.plot(x="Date", y="Price", ax=ax, label=year)
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.set_title("Average Price by Year")
ax.legend()
plt.show()

Chart the Number of Launches over Time by the Top 10 Organisations.¶

How has the dominance of launches changed over time between the different players?

In [74]:
df.head()
Out[74]:
Organisation Location Date Detail Rocket_Status Price Mission_Status Country Year Month year
0 SpaceX LC-39A, Kennedy Space Center, Florida, USA Fri Aug 07, 2020 05:12 UTC Falcon 9 Block 5 | Starlink V1 L9 & BlackSky StatusActive 50.0 Success USA 2020 Aug 2020
1 CASC Site 9401 (SLS-2), Jiuquan Satellite Launch Ce... Thu Aug 06, 2020 04:01 UTC Long March 2D | Gaofen-9 04 & Q-SAT StatusActive 29.75 Success China 2020 Aug 2020
2 SpaceX Pad A, Boca Chica, Texas, USA Tue Aug 04, 2020 23:57 UTC Starship Prototype | 150 Meter Hop StatusActive NaN Success USA 2020 Aug 2020
3 Roscosmos Site 200/39, Baikonur Cosmodrome, Kazakhstan Thu Jul 30, 2020 21:25 UTC Proton-M/Briz-M | Ekspress-80 & Ekspress-103 StatusActive 65.0 Success Kazakhstan 2020 Jul 2020
4 ULA SLC-41, Cape Canaveral AFS, Florida, USA Thu Jul 30, 2020 11:50 UTC Atlas V 541 | Perseverance StatusActive 145.0 Success USA 2020 Jul 2020
In [70]:
groups = avg_price.groupby("Year")
fig, ax = plt.subplots(figsize=(12, 8))

# Initialize the color palette
colors = plt.cm.tab10.colors

for i, (year, group) in enumerate(groups):
    # Set the alpha value based on whether the year is selected or not
    alpha = 1.0 if i == 0 else 0.3

    # Plot the data for the year with the specified color and alpha value
    group.plot(x="Date", y="Price", ax=ax, label=year, color=colors[i%10], alpha=alpha)

# Set labels and title
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.set_title("Average Price by Year")

# Show the legend
ax.legend()

# Function to update the alpha values on click
def onclick(event):
    # Get the index of the clicked line
    index = event.ind[0]

    # Update the alpha values of all lines
    for i, line in enumerate(ax.lines):
        line.set_alpha(1.0 if i == index else 0.3)

    # Redraw the figure
    fig.canvas.draw()

# Connect the onclick event to the figure
fig.canvas.mpl_connect('pick_event', onclick)

# Display the plot
plt.show()
In [120]:
top_10_org = pd.DataFrame(columns=df.columns)

for val in df.groupby("Organisation").count().sort_values("Date",ascending=False)[:10].index:
    print(val)
    org = df[df.Organisation == val]
    top_10_org = pd.concat([top_10, org], ignore_index=True)

top_10_org

RVSN USSR
Arianespace
General Dynamics
CASC
NASA
VKS RF
US Air Force
ULA
Boeing
Martin Marietta
Out[120]:
Organisation Location Date Detail Rocket_Status Price Mission_Status Country Year Month year
0 RVSN USSR Site 41/1, Plesetsk Cosmodrome, Russia Wed Aug 12, 1998 12:53 UTC Molniya-M /Block ML | Molniya-1 n†­133 StatusRetired NaN Success Russia 1998 8 1998
1 RVSN USSR Site 43/3, Plesetsk Cosmodrome, Russia Thu Aug 29, 1996 05:22 UTC Molniya-M /Block SO-L | Interbol 2, Magion5 & ... StatusRetired NaN Success Russia 1996 8 1996
2 RVSN USSR Site 43/3, Plesetsk Cosmodrome, Russia Wed Aug 02, 1995 23:59 UTC Molniya-M /Block SO-L | Interbol 1 & Magion 4 StatusRetired NaN Success Russia 1995 8 1995
3 RVSN USSR Site 32/1, Plesetsk Cosmodrome, Russia Mon Jul 13, 1992 17:41 UTC Tsyklon-3 | Cosmos 2197 to 2202 StatusRetired NaN Success Russia 1992 7 1992
4 RVSN USSR Site 43/3, Plesetsk Cosmodrome, Russia Wed Jul 08, 1992 09:53 UTC Molniya-M /Block 2BL | Cosmos 2196 StatusRetired NaN Success Russia 1992 7 1992
... ... ... ... ... ... ... ... ... ... ... ...
3622 Martin Marietta SLC-41, Cape Canaveral AFS, Florida, USA Fri Aug 26, 1966 13:59 UTC Titan IIIC | IDCSP-1 8-14, GGTS-2 StatusRetired NaN Failure USA 1966 8 1966
3623 Martin Marietta SLC-41, Cape Canaveral AFS, Florida, USA Thu Jun 16, 1966 14:00 UTC Titan IIIC | OPS 9311-9317 & GGTS-1 StatusRetired NaN Success USA 1966 6 1966
3624 Martin Marietta SLC-41, Cape Canaveral AFS, Florida, USA Tue Dec 21, 1965 14:00 UTC Titan IIIC | LES 3 & 4, OV2-3, OSCAR-4 StatusRetired NaN Partial Failure USA 1965 12 1965
3625 Martin Marietta SLC-40, Cape Canaveral AFS, Florida, USA Fri Oct 15, 1965 17:23 UTC Titan IIIC | LCS-2 & OV2-1 StatusRetired NaN Failure USA 1965 10 1965
3626 Martin Marietta SLC-40, Cape Canaveral AFS, Florida, USA Fri Jun 18, 1965 14:00 UTC Titan IIIC | Transtage 5 StatusRetired NaN Success USA 1965 6 1965

3627 rows × 11 columns

In [160]:
def extract_year(date_str):
    try:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y %H:%M %Z")
    except ValueError:
        dt = datetime.datetime.strptime(date_str, "%a %b %d, %Y")
    return dt.decade
In [161]:
df[df.Organisation=="CASC"]
top_10_org.groupby("Organisation").count().sort_values("Date",ascending=False)[:10].index
px.histogram(top_10_org.sort_values(by=["Organisation", "Date"], ascending=[True, False]),
             x="Organisation",
             color='Organisation',
             nbins=10)
In [ ]:

Cold War Space Race: USA vs USSR¶

The cold war lasted from the start of the dataset up until 1991.

In [189]:
df['Country'].unique()
countries_to_replace = ['Kazakhstan', 'Russia']
df.loc[df['Country'].isin(countries_to_replace), 'Country'] = 'Russia'
Out[189]:
0          USA
1        China
2          USA
3       Russia
4          USA
         ...  
4319       USA
4320       USA
4321       USA
4322    Russia
4323    Russia
Name: Country, Length: 4324, dtype: object
In [190]:
CW_df = df[(df['Country']=='USA') | (df['Country']=='Russia')]
In [191]:
war = CW_df.sort_values("Year")
war[(war.Year <= 1991)]
Out[191]:
Organisation Location Date Detail Rocket_Status Price Mission_Status Country Year Month year
4323 RVSN USSR Site 1/5, Baikonur Cosmodrome, Kazakhstan Fri Oct 04, 1957 19:28 UTC Sputnik 8K71PS | Sputnik-1 StatusRetired NaN Success Russia 1957 10 1957
4322 RVSN USSR Site 1/5, Baikonur Cosmodrome, Kazakhstan Sun Nov 03, 1957 02:30 UTC Sputnik 8K71PS | Sputnik-2 StatusRetired NaN Success Russia 1957 11 1957
4321 US Navy LC-18A, Cape Canaveral AFS, Florida, USA Fri Dec 06, 1957 16:44 UTC Vanguard | Vanguard TV3 StatusRetired NaN Failure USA 1957 12 1957
4293 US Air Force LC-11, Cape Canaveral AFS, Florida, USA Thu Dec 18, 1958 23:02 UTC SM-65B Atlas | SCORE StatusRetired NaN Success USA 1958 12 1958
4294 AMBA LC-5, Cape Canaveral AFS, Florida, USA Sat Dec 06, 1958 05:44 UTC Juno II | Pioneer 3 StatusRetired NaN Partial Failure USA 1958 12 1958
... ... ... ... ... ... ... ... ... ... ... ...
1755 NASA LC-39A, Kennedy Space Center, Florida, USA Sun Apr 28, 1991 11:33 UTC Space Shuttle Discovery | STS-39 StatusRetired 450.0 Success USA 1991 4 1991
1754 General Dynamics SLC-3W, Vandenberg AFB, California, USA Tue May 14, 1991 15:52 UTC Atlas-E/F Star-37S-ISS | NOAA-D StatusRetired NaN Success USA 1991 5 1991
1753 RVSN USSR Site 32/2, Plesetsk Cosmodrome, Russia Thu May 16, 1991 21:40 UTC Tsyklon-3 | Cosmos 2143 to 2148 StatusRetired NaN Success Russia 1991 5 1991
1762 RVSN USSR Site 43/3, Plesetsk Cosmodrome, Russia Fri Mar 22, 1991 12:19 UTC Molniya-M /Block ML | Molniya-3 n†­148 StatusRetired NaN Success Russia 1991 3 1991
1751 RVSN USSR Site 32/2, Plesetsk Cosmodrome, Russia Tue Jun 04, 1991 09:00 UTC Tsyklon-3 | Okean 3 StatusRetired NaN Success Russia 1991 6 1991

2432 rows × 11 columns

Create a Plotly Pie Chart comparing the total number of launches of the USSR and the USA¶

Hint: Remember to include former Soviet Republics like Kazakhstan when analysing the total number of launches.

In [192]:
CW_df["Country"].value_counts().rename_axis("Country").reset_index(name='counts')
Out[192]:
Country counts
0 Russia 2096
1 USA 1344
In [199]:
colors = ["purple", "orange"]
grouping = CW_df.groupby("Country").count().reset_index()
sizes = grouping['Mission_Status']
labels = grouping['Country']

plt.pie(sizes, labels = labels, colors = colors)
Out[199]:
([<matplotlib.patches.Wedge at 0x7f5194b95150>,
  <matplotlib.patches.Wedge at 0x7f5194b95960>],
 [Text(-0.37034241843976107, 1.0357830337981933, 'Russia'),
  Text(0.3703425154167657, -1.035782999124229, 'USA')])

Create a Chart that Shows the Total Number of Launches Year-On-Year by the Two Superpowers¶

In [202]:
CW_df.groupby(["year", "Country"]).size().unstack().plot()
Out[202]:
<Axes: xlabel='year'>

Chart the Total Number of Mission Failures Year on Year.¶

In [206]:
mission_failures = CW_df[CW_df['Mission_Status'] == 'Failure']
failures_yearly = mission_failures.groupby('Year').size()

plt.figure(figsize=(12, 8))
failures_yearly.plot(kind='bar', color='red')
plt.xlabel('Year')
plt.ylabel('Number of Mission Failures')
plt.title('Total Number of Mission Failures Year on Year (Bar Plot)')
plt.show()

plt.figure(figsize=(12, 8))
failures_yearly.plot(kind='line', marker='o', color='blue')
plt.xlabel('Year')
plt.ylabel('Number of Mission Failures')
plt.title('Total Number of Mission Failures Year on Year (Line Plot)')
plt.grid(True)
plt.show()

Chart the Percentage of Failures over Time¶

Did failures go up or down over time? Did the countries get better at minimising risk and improving their chances of success over time?

In [210]:
total_missions = CW_df.groupby('year').size()
failures = CW_df[CW_df['Mission_Status'] == 'Failure'].groupby('year').size()
failure_percentage = (failures / total_missions) * 100

failure_percentage.plot(kind='line', figsize=(10, 6))
plt.title('Percentage of Failures over Time')
plt.xlabel('Year')
plt.ylabel('Failure Percentage')
plt.show()

For Every Year Show which Country was in the Lead in terms of Total Number of Launches up to and including including 2020)¶

Do the results change if we only look at the number of successful launches?

In [217]:
df.Country.nunique()
Out[217]:
21
In [218]:
df.head()
Out[218]:
Organisation Location Date Detail Rocket_Status Price Mission_Status Country Year Month year
0 SpaceX LC-39A, Kennedy Space Center, Florida, USA Fri Aug 07, 2020 05:12 UTC Falcon 9 Block 5 | Starlink V1 L9 & BlackSky StatusActive 50.0 Success USA 2020 8 2020
1 CASC Site 9401 (SLS-2), Jiuquan Satellite Launch Ce... Thu Aug 06, 2020 04:01 UTC Long March 2D | Gaofen-9 04 & Q-SAT StatusActive 29.75 Success China 2020 8 2020
2 SpaceX Pad A, Boca Chica, Texas, USA Tue Aug 04, 2020 23:57 UTC Starship Prototype | 150 Meter Hop StatusActive NaN Success USA 2020 8 2020
3 Roscosmos Site 200/39, Baikonur Cosmodrome, Kazakhstan Thu Jul 30, 2020 21:25 UTC Proton-M/Briz-M | Ekspress-80 & Ekspress-103 StatusActive 65.0 Success Russia 2020 7 2020
4 ULA SLC-41, Cape Canaveral AFS, Florida, USA Thu Jul 30, 2020 11:50 UTC Atlas V 541 | Perseverance StatusActive 145.0 Success USA 2020 7 2020
In [224]:
top_countries = []

for year in df['Year'].unique():
    year_data = df[df['Year'] == year]
    top_country = year_data['Country'].value_counts().idxmax()
    top_countries.append((year, top_country))
    
top_countries_df = pd.DataFrame(top_countries, columns=['Year', 'Top Country'])

plt.figure(figsize=(10, 6))
plt.bar(top_countries_df['Year'], top_countries_df['Top Country'])
plt.xlabel('Year')
plt.ylabel('Top Country')
plt.title('Top Country with Most Launches Each Year')

plt.xticks(rotation=90)
plt.show()

Create a Year-on-Year Chart Showing the Organisation Doing the Most Number of Launches¶

Which organisation was dominant in the 1970s and 1980s? Which organisation was dominant in 2018, 2019 and 2020?

In [249]:
org_launches = df.groupby("year")["Organisation"].value_counts().rename_axis(["year", "Organisation"]).reset_index(name='counts')
org_launches.loc[org_launches.groupby("year")["counts"].idxmax()]
org_launches.head()
Out[249]:
year Organisation counts
0 1957 RVSN USSR 2
1 1957 US Navy 1
2 1958 US Navy 12
3 1958 AMBA 7
4 1958 RVSN USSR 5
In [251]:
org_set = set(org_launches['Organisation'])

plt.figure(figsize=(12, 10), dpi=80)
for org in org_set:
    selected_data = org_launches.loc[org_launches['Organisation'] == org]
    plt.plot(selected_data['year'], selected_data['counts'], label=org)
     
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
          fancybox=True, shadow=True, ncol=6)
plt.show()
In [ ]: